Pub Date : 2025-09-01Epub Date: 2025-09-06DOI: 10.1016/j.ejrs.2025.08.005
Rana Muhammad Amir Latif , Adnan Arshad , Jinliao He , Muhammad Habib Ur-Rahman , Fatma Mansour , Ayman El Sabagh , Ibrahim Al-Ashkar
Soil salinization poses a major threat to global agricultural productivity, degrading over 1.5 billion hectares of farmland worldwide. In Pakistan alone, approximately 5.7 million hectares of arable land nearly 30 % of the country’s irrigated area are affected by salinity, leading to substantial crop yield losses. Here, we demonstrate the potential of integrating Remote Sensing (RS) and Machine Learning (ML) to map soil salinity precisely. Using Sentinel-2A and Landsat-8 OLI data, combined with ground measurements of Electrical Conductivity (EC), we trained and validated three ML algorithms: Random Forest (RF), Classification and Regression Tree (CART), and Support Vector Regression (SVR). Through a refined selection process, we identified SI1, SI4, SI5, CRSI, and wetness as the most relevant indicators for soil salinity mapping. Our results show that RF outperforms CART and SVR, achieving R2 values of 0.91 (Sentinel-2A) and 0.86 (Landsat-8). The RF maps accurately depicted salt-affected lands, including the Indus River, swamp areas, agricultural fields, and saltpan areas. We estimate that 179,200 ha (Landsat-8) to 207,300 ha (Sentinel-2A) are affected by salinity. This study highlights the applications and integrations of RS and ML for monitoring soil salinity, providing location-specific real-time information for assessing unproductive land and to develop smart management practices and strategies for effective decision making.
{"title":"Integrating machine learning with multitemporal remote sensing to quantify spatial soil salinity","authors":"Rana Muhammad Amir Latif , Adnan Arshad , Jinliao He , Muhammad Habib Ur-Rahman , Fatma Mansour , Ayman El Sabagh , Ibrahim Al-Ashkar","doi":"10.1016/j.ejrs.2025.08.005","DOIUrl":"10.1016/j.ejrs.2025.08.005","url":null,"abstract":"<div><div>Soil salinization poses a major threat to global agricultural productivity, degrading over 1.5 billion hectares of farmland worldwide. In Pakistan alone, approximately 5.7 million hectares of arable land nearly 30 % of the country’s irrigated area are affected by salinity, leading to substantial crop yield losses. Here, we demonstrate the potential of integrating Remote Sensing (RS) and Machine Learning (ML) to map soil salinity precisely. Using Sentinel-2A and Landsat-8 OLI data, combined with ground measurements of Electrical Conductivity (EC), we trained and validated three ML algorithms: Random Forest (RF), Classification and Regression Tree (CART), and Support Vector Regression (SVR). Through a refined selection process, we identified SI1, SI4, SI5, CRSI, and wetness as the most relevant indicators for soil salinity mapping. Our results show that RF outperforms CART and SVR, achieving R<sup>2</sup> values of 0.91 (Sentinel-2A) and 0.86 (Landsat-8). The RF maps accurately depicted salt-affected lands, including the Indus River, swamp areas, agricultural fields, and saltpan areas. We estimate that 179,200 ha (Landsat-8) to 207,300 ha (Sentinel-2A) are affected by salinity. This study highlights the applications and integrations of RS and ML for monitoring soil salinity, providing location-specific real-time information for assessing unproductive land and to develop smart management practices and strategies for effective decision making.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 3","pages":"Pages 573-586"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-06-27DOI: 10.1016/j.ejrs.2025.06.003
Valisoasarobidy José Gabriel , Ruihong Wang , Doshroth Mahato , Can Wei
Slope stability and disaster mechanisms are critical concerns for the Honghe Hani Terraces (HHT), a UNESCO World Heritage Site renowned for its unique agricultural and cultural heritage. This systematic review examines the factors influencing slope instability, the role of climatic conditions, and the impact of agricultural practices in the region. Using the PRISMA framework, 105 studies from 2000 to 2023 were analyzed, identifying key trends and research gaps through bibliometric and thematic analyses. The findings reveal that natural factors, such as rainfall intensity and soil properties, interact with anthropogenic factors, including land use changes and traditional farming practices, to significantly influence slope stability. While traditional agricultural techniques like terracing can enhance soil conservation, improper management and recent land use changes, such as deforestation and urbanization, have intensified instability. Numerical simulations highlight the complex interplay between rainfall, irrigation, and slope dynamics, emphasizing the need for integrated management strategies. The review underscores the importance of combining traditional knowledge with modern technologies, such as remote sensing and GIS, to develop sustainable land management practices and early warning systems. Community involvement and capacity-building are also essential for effective mitigation. Despite limitations, such as methodological variability and data inconsistencies, this review provides a comprehensive understanding of slope stability in the HHT and proposes future research directions to enhance disaster resilience and preserve this unique cultural landscape.
{"title":"Slope stability and disaster mechanisms in the Honghe Hani Terraces: a systematic review","authors":"Valisoasarobidy José Gabriel , Ruihong Wang , Doshroth Mahato , Can Wei","doi":"10.1016/j.ejrs.2025.06.003","DOIUrl":"10.1016/j.ejrs.2025.06.003","url":null,"abstract":"<div><div>Slope stability and disaster mechanisms are critical concerns for the Honghe Hani Terraces (HHT), a UNESCO World Heritage Site renowned for its unique agricultural and cultural heritage. This systematic review examines the factors influencing slope instability, the role of climatic conditions, and the impact of agricultural practices in the region. Using the PRISMA framework, 105 studies from 2000 to 2023 were analyzed, identifying key trends and research gaps through bibliometric and thematic analyses. The findings reveal that natural factors, such as rainfall intensity and soil properties, interact with anthropogenic factors, including land use changes and traditional farming practices, to significantly influence slope stability. While traditional agricultural techniques like terracing can enhance soil conservation, improper management and recent land use changes, such as deforestation and urbanization, have intensified instability. Numerical simulations highlight the complex interplay between rainfall, irrigation, and slope dynamics, emphasizing the need for integrated management strategies. The review underscores the importance of combining traditional knowledge with modern technologies, such as remote sensing and GIS, to develop sustainable land management practices and early warning systems. Community involvement and capacity-building are also essential for effective mitigation. Despite limitations, such as methodological variability and data inconsistencies, this review provides a comprehensive understanding of slope stability in the HHT and proposes future research directions to enhance disaster resilience and preserve this unique cultural landscape.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 3","pages":"Pages 411-425"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-07-14DOI: 10.1016/j.ejrs.2025.07.002
Valisoasarobidy José Gabriel , Ruihong Wang , Doshrot Mahato , Can Wei
Landslide susceptibility mapping is critical for risk assessment, but existing ensemble methods like VotingClassifier suffer from three unresolved limitations: static weight allocation that ignores spatial variability, lack of quantifiable uncertainty measures, and poor integration of interpretability tools. This study introduces a novel weighted average ensemble method that dynamically adjusts weights for Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) through 5-fold spatial cross-validation, improving prediction robustness across Yuanyang County’s 2240 km2 of mountainous terrain (23°05′–23°15′N, 102°40′–102°50′E) with 817 validated landslides. The method tackles important issues by combining the best features of strong models while reducing the effects of related variables using composite indices (like a soil-lithology index based on a Pearson correlation of r = 0.81), backed by a thorough preprocessing process that includes Moran’s I-validated stratified sampling (I = 0.12), normalization that accounts for outliers (95th percentile), and spatial division with 500 m buffers. The novel ensemble achieved an accuracy of 84.32 % and an ROC AUC of 91.96 %, with sensitivity analysis via SHAP (SHapley Additive exPlanations) identifying rainfall (21 %), distance index (13 %), and elevation slope index (27 %) as dominant drivers, while uncertainty analysis revealed prediction intervals of ±0.62 width (95 % coverage). The resulting maps, validated through spatial consistency checks (AUC > 0.84), provide actionable tools for high-risk zones. This research improves landslide susceptibility mapping by developing a dynamic, uncertainty-based system that rectifies major weaknesses in static ensemble methods, thereby establishing a replicable standard for future investigations.
{"title":"A novel weighted average ensemble method for landslide susceptibility mapping: A case study in Yuanyang, China","authors":"Valisoasarobidy José Gabriel , Ruihong Wang , Doshrot Mahato , Can Wei","doi":"10.1016/j.ejrs.2025.07.002","DOIUrl":"10.1016/j.ejrs.2025.07.002","url":null,"abstract":"<div><div>Landslide susceptibility mapping is critical for risk assessment, but existing ensemble methods like VotingClassifier suffer from three unresolved limitations: static weight allocation that ignores spatial variability, lack of quantifiable uncertainty measures, and poor integration of interpretability tools. This study introduces a novel weighted average ensemble method that dynamically adjusts weights for Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) through 5-fold spatial cross-validation, improving prediction robustness across Yuanyang County’s 2240 km<sup>2</sup> of mountainous terrain (23°05′–23°15′N, 102°40′–102°50′E) with 817 validated landslides. The method tackles important issues by combining the best features of strong models while reducing the effects of related variables using composite indices (like a soil-lithology index based on a Pearson correlation of r = 0.81), backed by a thorough preprocessing process that includes Moran’s I-validated stratified sampling (I = 0.12), normalization that accounts for outliers (95th percentile), and spatial division with 500 m buffers. The novel ensemble achieved an accuracy of 84.32 % and an ROC AUC of 91.96 %, with sensitivity analysis via SHAP (SHapley Additive exPlanations) identifying rainfall (21 %), distance index (13 %), and elevation slope index (27 %) as dominant drivers, while uncertainty analysis revealed prediction intervals of ±0.62 width (95 % coverage). The resulting maps, validated through spatial consistency checks (AUC > 0.84), provide actionable tools for high-risk zones. This research improves landslide susceptibility mapping by developing a dynamic, uncertainty-based system that rectifies major weaknesses in static ensemble methods, thereby establishing a replicable standard for future investigations.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 3","pages":"Pages 436-454"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-03-20DOI: 10.1016/j.ejrs.2025.03.001
Amanda Tri Persada , Yulius , Syamsul B. Agus , Hadiwijaya L. Salim , Ira Dillenia , Taslim Arifin , Aida Heriati , Joko Prihantono , Dini Purbani , Sri Endah Purnamaningtyas , Didik Wahju Hendro Tjahjo , Muhammad Ramdhan , Siti Hajar Suryawati , Ary Wahyono , Ulung Jantama Wisha , Zulfiandi , Fery Kurniawan
The significance of this research lies in its contribution to Olipier cultural site vulnerability caused by coastal erosion and climate change impacts in East Belitung, Indonesia. Therefore, this study employs the Coastal Vulnerability Index (CVI) and Cultural Resource Vulnerability (CRV) methods to assess coastal vulnerability and site susceptibility, which integrates physical parameters, such as elevation, beach slope, geomorphology, land use, tidal range, significant wave height, shoreline change, distance from shoreline to sites, and sea-level rise. The CVI analysis results indicate that approximately 12.68 km of the observed coastline is very highly vulnerable, 8.72 km is highly vulnerable, and the remnant 10.91 km coastline is categorized as low vulnerability. On the other hand, the CRV method emphasizes specific vulnerable locations, identifying that approximately 53.34 % oil refineries are highly vulnerable zones due to their proximity to the shoreline, low elevation, and slope. This study also underscores the importance of proactive conservation measures, whereby implementing coastal protection structures, mangrove rehabilitation, and coral reef transplantation are possible. Collaboration between local and central governments is essential for effective coastal management and conservation of cultural heritage sites. Overall, this research provides valuable insights for coastal management strategies to mitigate risks and preserve cultural heritage in East Belitung Regency.
{"title":"Olipier cultural site vulnerability analysis in East Belitung, Indonesia: Cultural resources vulnerability (CRV) methods","authors":"Amanda Tri Persada , Yulius , Syamsul B. Agus , Hadiwijaya L. Salim , Ira Dillenia , Taslim Arifin , Aida Heriati , Joko Prihantono , Dini Purbani , Sri Endah Purnamaningtyas , Didik Wahju Hendro Tjahjo , Muhammad Ramdhan , Siti Hajar Suryawati , Ary Wahyono , Ulung Jantama Wisha , Zulfiandi , Fery Kurniawan","doi":"10.1016/j.ejrs.2025.03.001","DOIUrl":"10.1016/j.ejrs.2025.03.001","url":null,"abstract":"<div><div>The significance of this research lies in its contribution to Olipier cultural site vulnerability caused by coastal erosion and climate change impacts in East Belitung, Indonesia. Therefore, this study employs the Coastal Vulnerability Index (CVI) and Cultural Resource Vulnerability (CRV) methods to assess coastal vulnerability and site susceptibility, which integrates physical parameters, such as elevation, beach slope, geomorphology, land use, tidal range, significant wave height, shoreline change, distance from shoreline to sites, and sea-level rise. The CVI analysis results indicate that approximately 12.68 km of the observed coastline is very highly vulnerable, 8.72 km is highly vulnerable, and the remnant 10.91 km coastline is categorized as low vulnerability. On the other hand, the CRV method emphasizes specific vulnerable locations, identifying that approximately 53.34 % oil refineries are highly vulnerable zones due to their proximity to the shoreline, low elevation, and slope. This study also underscores the importance of proactive conservation measures, whereby implementing coastal protection structures, mangrove rehabilitation, and coral reef transplantation are possible. Collaboration between local and central governments is essential for effective coastal management and conservation of cultural heritage sites. Overall, this research provides valuable insights for coastal management strategies to mitigate risks and preserve cultural heritage in East Belitung Regency.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 167-184"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate crop types and land cover maps are pivotal for effective land management and agricultural policy, particularly in regions with complex agricultural landscapes and small field sizes. Northeast Thailand, a significant agricultural hub, faces challenges in crop classification due to its diverse crop patterns, cloud cover, and smallholder plots. This study integrates satellite data from PRISMA, Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8/9 (L8/9) imagery to address these challenges. A total of 1305 reference point were randomly collected between November and December 2022 to train and validate the proposed crop classification. Specifically, 15 different combinations using a random forest (RF) classifier were tested. The combination of all datasets achieved the highest overall accuracy (OA) of 91.5 %, followed by S1 + S2 + L8/9 (89.8 %), while PRISMA alone yielded a lower accuracy (63.8 %). The study identified nine dominant land cover classes, with cassava, rice, and sugarcane as primary crops. A strong correlation (r = 0.91) with the official Land Development Department (LDD) statistics demonstrates the robustness of the method. This research highlights the technical advantage of multi-sensor integration in overcoming the limitations of single-sensor data, providing a reliable tool for accurate crop mapping, and supporting sustainable agricultural practices in challenging environments.
{"title":"Integrating PRISMA hyperspectral data with Sentinel-1, Sentinel-2 and Landsat data for mapping crop types and land cover in northeast Thailand","authors":"Savittri Ratanopad Suwanlee , Zahid Naeem Qaisrani , Jaturong Som-ard , Surasak Keawsomsee , Kemin Kasa , Narissara Nuthammachot , Siwa Kaewplang , Sarawut Ninsawat , Enrico Borgogno Mondino , Samuele De Petris , Filippo Sarvia","doi":"10.1016/j.ejrs.2025.04.005","DOIUrl":"10.1016/j.ejrs.2025.04.005","url":null,"abstract":"<div><div>Accurate crop types and land cover maps are pivotal for effective land management and agricultural policy, particularly in regions with complex agricultural landscapes and small field sizes. Northeast Thailand, a significant agricultural hub, faces challenges in crop classification due to its diverse crop patterns, cloud cover, and smallholder plots. This study integrates satellite data from PRISMA, Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8/9 (L8/9) imagery to address these challenges. A total of 1305 reference point were randomly collected between November and December 2022 to train and validate the proposed crop classification. Specifically, 15 different combinations using a random forest (RF) classifier were tested. The combination of all datasets achieved the highest overall accuracy (OA) of 91.5 %, followed by S1 + S2 + L8/9 (89.8 %), while PRISMA alone yielded a lower accuracy (63.8 %). The study identified nine dominant land cover classes, with cassava, rice, and sugarcane as primary crops. A strong correlation (r = 0.91) with the official Land Development Department (LDD) statistics demonstrates the robustness of the method. This research highlights the technical advantage of multi-sensor integration in overcoming the limitations of single-sensor data, providing a reliable tool for accurate crop mapping, and supporting sustainable agricultural practices in challenging environments.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 252-260"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-03-12DOI: 10.1016/j.ejrs.2025.02.003
Muhammad Zainuddin Lubis , Muhammad Ghazali , Andrean V.H. Simanjuntak , Nelly F. Riama , Gumilang R. Pasma , Asep Priatna , Husnul Kausarian , Made Wedanta Suryadarma , Sri Pujiyati , Fredrich Simanungkalit , Batara , Kutubuddin Ansari , Punyawi Jamjareegulgarn
Our study investigates the decadal and seasonal variability of sea surface height (SSH) and sea surface temperature (SST) in the Gulf of Thailand (GoT) using data from CMEMS from 1993 to 2021. We employed statistical analyses utilizing GLM and GAM to assess the variables comprehensively. The reveals a significant upward trend in SSH, increasing from ∼0.79 m in 1993–1998 to ∼0.89 m in 2017–2021, highlighting the impacts of climate change. SST analysis revealed fluctuations, with a maximum reaching ∼30.6 °C in 2019–2020, correlating with climatic events such as El Niño. Our study results at station 1 (near Bangkok) showed that the average SSH in 1998 during strong El Niño years was equal to 0.82 m, while the maximum SST was equal to 29.89 °C. Seasonal patterns indicated SSH peaks in DJF and SON at ∼0.92 m, while SST peaked in spring MAM and summer JJA at ∼30.7 °C. Volume transport analysis showed significant variability, with 0.3634 Sv (0–55 m) at longitude 99°E-107° E and latitude 6° N, indicating complex circulation patterns influenced by bathymetry and wind. Time series analysis revealed an average SSH increase of 0.0038 m/year, with a high pseudo-R-squared of 0.99. Our findings underscore the critical influence of climate variability on oceanographic conditions in the GoT, emphasizing the need for ongoing monitoring to address the implications of rising sea levels and temperature fluctuations. In conjunction with increased SSH, the rising SST heightens the risk of flooding in low-lying areas, exacerbating vulnerabilities for local populations and necessitating adaptive management strategies to mitigate these impacts.
{"title":"Decadal and seasonal oceanographic trends influenced by climate changes in the Gulf of Thailand","authors":"Muhammad Zainuddin Lubis , Muhammad Ghazali , Andrean V.H. Simanjuntak , Nelly F. Riama , Gumilang R. Pasma , Asep Priatna , Husnul Kausarian , Made Wedanta Suryadarma , Sri Pujiyati , Fredrich Simanungkalit , Batara , Kutubuddin Ansari , Punyawi Jamjareegulgarn","doi":"10.1016/j.ejrs.2025.02.003","DOIUrl":"10.1016/j.ejrs.2025.02.003","url":null,"abstract":"<div><div>Our study investigates the decadal and seasonal variability of sea surface height (SSH) and sea surface temperature (SST) in the Gulf of Thailand (GoT) using data from CMEMS from 1993 to 2021. We employed statistical analyses utilizing GLM and GAM to assess the variables comprehensively. The reveals a significant upward trend in SSH, increasing from ∼0.79 m in 1993–1998 to ∼0.89 m in 2017–2021, highlighting the impacts of climate change. SST analysis revealed fluctuations, with a maximum reaching ∼30.6 °C in 2019–2020, correlating with climatic events such as El Niño. Our study results at station 1 (near Bangkok) showed that the average SSH in 1998 during strong El Niño years was equal to 0.82 m, while the maximum SST was equal to 29.89 °C. Seasonal patterns indicated SSH peaks in DJF and SON at ∼0.92 m, while SST peaked in spring MAM and summer JJA at ∼30.7 °C. Volume transport analysis showed significant variability, with 0.3634 Sv (0–55 m) at longitude 99°E-107° E and latitude 6° N, indicating complex circulation patterns influenced by bathymetry and wind. Time series analysis revealed an average SSH increase of 0.0038 m/year, with a high pseudo-R-squared of 0.99. Our findings underscore the critical influence of climate variability on oceanographic conditions in the GoT, emphasizing the need for ongoing monitoring to address the implications of rising sea levels and temperature fluctuations. In conjunction with increased SSH, the rising SST heightens the risk of flooding in low-lying areas, exacerbating vulnerabilities for local populations and necessitating adaptive management strategies to mitigate these impacts.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 151-166"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-05-15DOI: 10.1016/j.ejrs.2025.05.007
Yousef Bahrami, Hossein Hassani, Abbas Maghsoudi
The southeastern portion of the Urumieh–Dokhtar magmatic arc (UDMA), known as Kerman Cenozoic magmatic arc (KCMA), is a major host to world-class giant to subeconomic small porphyry copper deposits (PCDs) in Iran. As the KCMA is characterized by well-exposed rocks and sparsely vegetated surfaces, it is an intriguing region for geological remote sensing studies. In particular, mixed pixels are a key source of annoyance in traditional image classification because of a sensor’s immediate field of view restriction and the variety of land cover classes. By evaluating the observed spectrum of mixed pixels, sub-pixel mapping techniques can decompose each mixed pixel and determine the proportion of each component class, and so a classification map with a finer resolution is attainable. This paper endeavors to assess the capability and accuracy of linear spectral unmixing (LSU), multiple endmember spectral mixture analysis (MESMA), and mixture tuned target constrained interference minimized filter analysis (MTTCIMF) to investigate how well these sub-pixel algorithms could identify and map key hydrothermal alteration zones linked with PCDs in the Pariz–Chahargonbad area. Previous works have applied these algorithms widely to hyperspectral data, but few previous works have applied them to multispectral data such as ASTER. In this work, these algorithms were found helpful in the accurate identification of argillic, phyllic, and propylitic alteration zones per validation with field observations, petrographic studies and X-ray diffraction analysis of rock samples.
{"title":"Employing both full and partial sub-pixel mapping methods to delineate hydrothermal alteration zones associated with porphyry copper deposits","authors":"Yousef Bahrami, Hossein Hassani, Abbas Maghsoudi","doi":"10.1016/j.ejrs.2025.05.007","DOIUrl":"10.1016/j.ejrs.2025.05.007","url":null,"abstract":"<div><div>The southeastern portion of the Urumieh–Dokhtar magmatic arc (UDMA), known as Kerman Cenozoic magmatic arc (KCMA), is a major host to world-class giant to subeconomic small porphyry copper deposits (PCDs) in Iran. As the KCMA is characterized by well-exposed rocks and sparsely vegetated surfaces, it is an intriguing region for geological remote sensing studies. In particular, mixed pixels are a key source of annoyance in traditional image classification because of a sensor’s immediate field of view restriction and the variety of land cover classes. By evaluating the observed spectrum of mixed pixels, sub-pixel mapping techniques can decompose each mixed pixel and determine the proportion of each component class, and so a classification map with a finer resolution is attainable. This paper endeavors to assess the capability and accuracy of linear spectral unmixing (LSU), multiple endmember spectral mixture analysis (MESMA), and mixture tuned target constrained interference minimized filter analysis (MTTCIMF) to investigate how well these sub-pixel algorithms could identify and map key hydrothermal alteration zones linked with PCDs in the Pariz–Chahargonbad area. Previous works have applied these algorithms widely to hyperspectral data, but few previous works have applied them to multispectral data such as ASTER. In this work, these algorithms were found helpful in the accurate identification of argillic, phyllic, and propylitic alteration zones per validation with field observations, petrographic studies and X-ray diffraction analysis of rock samples.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 303-321"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-05-10DOI: 10.1016/j.ejrs.2025.05.003
Musa M.M. Mina , Ahmed A.A. Osman , Mohammed A.M. Alnour , Rowida A.M. Abdalla , Khalid A.E. Zeinelabdein , Samia Abdelrahman , Hassan K.E. Elawad , Gábor Kovács , Gabriella B. Kiss
The area of our research lies in the Red Sea Hills region in NE Sudan and occupies a central position in the Nubian part of the late Proterozoic Nubian-Arabian Shield. The Red Sea Hills have received considerable studies in structural and remote sensing aspects in the past decades. Most of the studies were conducted to understand the structural evolution and the tectonic development of the Nubian-Arabian Shield in northeast Sudan. However, the link between the structural elements and the mineralization in the area is not well established, and in several parts of the region the identification of mineral deposits is also not well known. Therefore, the present study deals mainly with the determination of mineralization zones and highlights the structural elements of the study area. The processing of Landsat 8 OLI images has included different methods such as band rationing, density slicing, and featured oriented principal component analysis. These methods allowed us to identify the zones of hydrothermal alteration, which could be associated with ore mineralization within the study area. These mapped alteration zones were verified with the aid of the obtained field and geochemical data. Interpretation of the detailed geochemical data set of the study area revealed the presence of Au/Cu/Zn anomalies at most of the perspective locations outlined in the hydrothermal composite map, uniquely supporting the usefulness of remote sensing methods. The structural analysis of the brittle deformation manifestations revealed that the NE–SW fracture system represents the main controlling factor on the occurrence of the mineralization in our research area.
{"title":"Remote sensing techniques for mapping hydrothermal alteration zones of volcanogenic massive sulfide deposits in Red Sea Hills, NE Sudan","authors":"Musa M.M. Mina , Ahmed A.A. Osman , Mohammed A.M. Alnour , Rowida A.M. Abdalla , Khalid A.E. Zeinelabdein , Samia Abdelrahman , Hassan K.E. Elawad , Gábor Kovács , Gabriella B. Kiss","doi":"10.1016/j.ejrs.2025.05.003","DOIUrl":"10.1016/j.ejrs.2025.05.003","url":null,"abstract":"<div><div>The area of our research lies in the Red Sea Hills region in NE Sudan and occupies a central position in the Nubian part of the late Proterozoic Nubian-Arabian Shield. The Red Sea Hills have received considerable studies in structural and remote sensing aspects in the past decades. Most of the studies were conducted to understand the structural evolution and the tectonic development of the Nubian-Arabian Shield in northeast Sudan. However, the link between the structural elements and the mineralization in the area is not well established, and in several parts of the region the identification of mineral deposits is also not well known. Therefore, the present study deals mainly with the determination of mineralization zones and highlights the structural elements of the study area. The processing of Landsat 8 OLI images has included different methods such as band rationing, density slicing, and featured oriented principal component analysis. These methods allowed us to identify the zones of hydrothermal alteration, which could be associated with ore mineralization within the study area. These mapped alteration zones were verified with the aid of the obtained field and geochemical data. Interpretation of the detailed geochemical data set of the study area revealed the presence of Au/Cu/Zn anomalies at most of the perspective locations outlined in the hydrothermal composite map, uniquely supporting the usefulness of remote sensing methods. The structural analysis of the brittle deformation manifestations revealed that the NE–SW fracture system represents the main controlling factor on the occurrence of the mineralization in our research area.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 280-294"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-05-08DOI: 10.1016/j.ejrs.2025.05.004
Mulat Amare Tshayu , Teshome Betru Tadesse , Kindu Setalem Meshesha , Mohammed Habib Afkea , Mohammed Motuma Assen
The alteration of land use/land cover change (LULCC) is an environmental issue that impacts affects ecosystems by increasing the land surface temperature (LST). This study aimed to investigate the influence of human activities on LST in the Sekota watershed northern Ethiopia. This study used Landsat images and a supervised support vector machine (SVM) classification algorithm to map LU/LC and estimate LST. The findings revealed that farmland exhibited the most substantial expansion, with a net gain of 16,970.84 ha, while shrubland experienced the most significant decline, with a net loss of 20,768.57 ha. Moreover, forest cover by 329.73 ha, bare land by 2048.97 ha, and settlements by 131.07 ha increased from 2000 to 2022. The mean LST increased from 32.31 °C in 2000 to 36.01 °C in 2014, followed by a gradual decrease to 34.18 °C in 2022. The overall accuracy and kappa coefficients of the LULC maps were 87.6 % (0.8421), 91.5 % (0.8901), and 92 % (0.8973) in 2000, 2014, and 2022, respectively. This study also investigated the correlation between the normalized difference vegetation index (NDVI) and LST. The results demonstrated a negative relationship, with correlation coefficient R2 values of 0.70, 0.65, and 0.75 for 2000, 2014, and 2022, respectively. This indicates that non-vegetated e areas had higher LST levels than forested areas. As a result, it is recommended that government agencies and local communities focus on preserving vegetation cover and adopting practices such as planting perennial fruit crops and implementing agroforestry systems in the study area.
{"title":"Examining human activities in response to land surface temperature in Sekota watershed, northern Ethiopia","authors":"Mulat Amare Tshayu , Teshome Betru Tadesse , Kindu Setalem Meshesha , Mohammed Habib Afkea , Mohammed Motuma Assen","doi":"10.1016/j.ejrs.2025.05.004","DOIUrl":"10.1016/j.ejrs.2025.05.004","url":null,"abstract":"<div><div>The alteration of land use/land cover change (LULCC) is an environmental issue that impacts affects ecosystems by increasing the land surface temperature (LST). This study aimed to investigate the influence of human activities on LST in the Sekota watershed northern Ethiopia. This study used Landsat images and a supervised support vector machine (SVM) classification algorithm to map LU/LC and estimate LST. The findings revealed that farmland exhibited the most substantial expansion, with a net gain of 16,970.84 ha, while shrubland experienced the most significant decline, with a net loss of 20,768.57 ha. Moreover, forest cover by 329.73 ha, bare land by 2048.97 ha, and settlements by 131.07 ha increased from 2000 to 2022. The mean LST increased from 32.31 °C in 2000 to 36.01 °C in 2014, followed by a gradual decrease to 34.18 °C in 2022. The overall accuracy and kappa coefficients of the LULC maps were 87.6 % (0.8421), 91.5 % (0.8901), and 92 % (0.8973) in 2000, 2014, and 2022, respectively. This study also investigated the correlation between the normalized difference vegetation index (NDVI) and LST. The results demonstrated a negative relationship, with correlation coefficient R<sup>2</sup> values of 0.70, 0.65, and 0.75 for 2000, 2014, and 2022, respectively. This indicates that non-vegetated e areas had higher LST levels than forested areas. As a result, it is recommended that government agencies and local communities focus on preserving vegetation cover and adopting practices such as planting perennial fruit crops and implementing agroforestry systems in the study area.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 261-271"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-05-16DOI: 10.1016/j.ejrs.2025.05.005
Shimaa Abd El-Monem , Ahmed Azouz , Alaaeldin S. Hassan , El-Sayed Soliman A. Said , Abdelhady A. Ammar
Synthetic Aperture Radar (SAR) is a widely utilized remote sensing technology, offering robust operational efficiency under all weather conditions and independent of daylight. Ideally, the SAR platform maintains a linear trajectory at a constant altitude and velocity. However, this idealization is compromised for spaceborne SAR systems, such as those in low Earth orbit (LEO), due to the satellite’s elliptical orbit, which introduces motion errors that degrade image focusing quality. This paper presents a novel approach to enhance first-order motion compensation (MOCO) by addressing the motion errors caused by elliptical orbital dynamics and perturbations. The proposed methodology involves applying three distinct fitting techniques to the invariant range error, a critical parameter in first-order MOCO, and optimizing phase gradients to determine the optimal coefficients for improving image quality metrics. Real-raw SAR data from the Sentinel-1 Level-0 dataset is processed to validate the proposed techniques, and the results are benchmarked against the corresponding Sentinel-1 Level-1 Single Look Complex (SLC) image. The validation is conducted through two approaches: first, image quality assessment using sharpness, contrast, and entropy metrics; and second, quantitative evaluation of azimuth-integrated sidelobe ratio (AISLR), azimuth peak sidelobe ratio (APSLR), and impulse response width (IRW) at two prominent reflective points. The findings indicate a marked enhancement in the image quality parameters, demonstrating the efficacy of the proposed motion compensation and optimization framework.
合成孔径雷达（SAR）是一种广泛应用的遥感技术，在全天候和不受日光影响的情况下提供强大的操作效率。理想情况下，SAR平台在恒定的高度和速度下保持线性轨迹。然而，由于卫星的椭圆轨道引入了运动误差，降低了图像聚焦质量，因此这种理想化的效果在星载SAR系统（如低地球轨道）中受到了损害。本文提出了一种新的方法，通过解决椭圆轨道动力学和摄动引起的运动误差来增强一阶运动补偿。提出的方法包括应用三种不同的拟合技术来处理不变距离误差，一阶MOCO中的一个关键参数，以及优化相位梯度以确定提高图像质量指标的最佳系数。对来自Sentinel-1 Level-0数据集的真实原始SAR数据进行处理以验证所提出的技术，并将结果与相应的Sentinel-1 Level-1 Single Look Complex （SLC）图像进行基准测试。通过两种方法进行验证：首先，使用清晰度，对比度和熵指标进行图像质量评估；定量评价两个突出反射点的方位角积分旁瓣比（AISLR）、方位角峰值旁瓣比（APSLR）和脉冲响应宽度（IRW）。结果表明，图像质量参数显著增强，证明了所提出的运动补偿和优化框架的有效性。
{"title":"Enhancing motion compensation in spaceborne SAR imaging","authors":"Shimaa Abd El-Monem , Ahmed Azouz , Alaaeldin S. Hassan , El-Sayed Soliman A. Said , Abdelhady A. Ammar","doi":"10.1016/j.ejrs.2025.05.005","DOIUrl":"10.1016/j.ejrs.2025.05.005","url":null,"abstract":"<div><div>Synthetic Aperture Radar (SAR) is a widely utilized remote sensing technology, offering robust operational efficiency under all weather conditions and independent of daylight. Ideally, the SAR platform maintains a linear trajectory at a constant altitude and velocity. However, this idealization is compromised for spaceborne SAR systems, such as those in low Earth orbit (LEO), due to the satellite’s elliptical orbit, which introduces motion errors that degrade image focusing quality. This paper presents a novel approach to enhance first-order motion compensation (MOCO) by addressing the motion errors caused by elliptical orbital dynamics and perturbations. The proposed methodology involves applying three distinct fitting techniques to the invariant range error, a critical parameter in first-order MOCO, and optimizing phase gradients to determine the optimal coefficients for improving image quality metrics. Real-raw SAR data from the Sentinel-1 Level-0 dataset is processed to validate the proposed techniques, and the results are benchmarked against the corresponding Sentinel-1 Level-1 Single Look Complex (SLC) image. The validation is conducted through two approaches: first, image quality assessment using sharpness, contrast, and entropy metrics; and second, quantitative evaluation of azimuth-integrated sidelobe ratio (AISLR), azimuth peak sidelobe ratio (APSLR), and impulse response width (IRW) at two prominent reflective points. The findings indicate a marked enhancement in the image quality parameters, demonstrating the efficacy of the proposed motion compensation and optimization framework.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 322-336"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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